A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG

Early detection of coronary heart disease (CHD) has the potential to prevent the millions of deaths that this disease causes worldwide every year. However, there exist few automatic methods to detect CHD at an early stage. A challenge in the development of these methods is the absence of relevant datasets for their training and validation. Here, the ten Tusscher-Panfilov 2006 model and the O’Hara-Rudy model for human myocytes were used to create two populations of models that were in concordance with data obtained from healthy individuals (control populations) and included inter-subject variability. The effects of ischemia were subsequently included in the control populations to simulate the effects of mild and severe ischemic events on single cells, full ischemic cables of cells and cables of cells with various sizes of ischemic regions. Action potential and pseudo-ECG biomarkers were measured to assess how the evolution of ischemia could be quantified. Finally, two neural network classifiers were trained to identify the different degrees of ischemia using the pseudo-ECG biomarkers. The control populations showed action potential and pseudo-ECG biomarkers within the physiological ranges and the trends in the biomarkers commonly identified in ischemic patients were observed in the ischemic populations. On the one hand, inter-subject variability in the ischemic pseudo-ECGs precluded the detection and classification of early ischemic events using any single biomarker. On the other hand, the neural networks showed sensitivity and positive predictive value above 95%. Additionally, the neural networks revealed that the biomarkers that were relevant for the detection of ischemia were different from those relevant for its classification. This work showed that a computational approach could be used, when data is scarce, to validate proof-of-concept machine learning methods to detect ischemic events.

冠状动脉心脏病（Coronary Heart Disease, CHD）的早期检测，可有效预防该疾病每年在全球范围内引发的数百万例死亡。然而，目前用于早期检测CHD的自动化方法仍较为匮乏。此类方法开发过程中面临的一大挑战，是缺乏用于模型训练与验证的相关数据集。本研究采用针对人类心肌细胞的ten Tusscher-Panfilov 2006模型与O’Hara-Rudy模型，构建了两组符合健康个体实测数据的模型种群（对照组种群），并纳入了个体间变异性。随后，研究将缺血（ischemia）效应引入对照组种群，以模拟轻度、重度缺血事件对单细胞、完全缺血细胞束以及携带不同尺寸缺血区域的细胞束所产生的影响。研究通过测量动作电位（action potential）与伪心电图（pseudo-ECG）生物标志物，来评估缺血演化过程的量化方式。最后，本研究训练了两个神经网络分类器，利用伪心电图生物标志物对不同程度的缺血进行识别。对照组种群的动作电位与伪心电图生物标志物均处于生理正常范围之内，且缺血种群中可观察到缺血患者常见的生物标志物变化趋势。一方面，缺血性伪心电图中的个体间变异性，使得仅依靠单一生物标志物无法实现早期缺血事件的检测与分类。另一方面，神经网络的灵敏度与阳性预测值均超过95%。此外，神经网络分析显示，与缺血检测相关的生物标志物，与用于缺血分级的生物标志物并不相同。本研究证实，在数据匮乏的场景下，可采用计算方法验证用于检测缺血事件的概念验证型机器学习方法。